背景:实施电子健康记录(EHR)系统是一项关键挑战,特别是在低收入国家,行为意图起着至关重要的作用。为了解决这个问题,我们进行了一项研究,以扩展和应用接受和使用技术3(UTAUT3)模型来预测卫生专业人员使用EHR系统的行为意图。
方法:在埃塞俄比亚西南部的423名卫生专业人员中采用了定量研究方法。我们通过测量和结构模型统计来评估所提出模型的有效性。使用SPSSAMOS版本23进行分析。使用结构方程模型(SEM)分析对假设进行了检验,并对调解和调节作用进行了评估。用标准化回归系数(β)检验外生变量和内生变量之间的关联,95%置信区间,和p值,P值<0.05的显著性水平。
结果:所提出的模型优于以前的UTAUT模型,解释使用EHR系统的行为意图方差的84.5%(平方多重相关(R2)=0.845)。个人创新能力(β=0.215,p值<0.018),性能预期(β=0.245,p值<0.001),和态度(β=0.611,p值<0.001)显示出使用EHR系统的显着关联。中介分析显示,预期业绩,享乐动机,技术焦虑对行为意向有显著的间接影响。此外,适度分析表明,性别调节了社会影响力之间的关系,个人创新,和行为意图。
结论:扩展的UTAUT3模型准确地预测了卫生专业人员使用EHR系统的意图,并为了解医疗保健中的技术接受提供了一个有价值的框架。我们建议数字卫生实施者和有关机构考虑全面的直接、间接,和调节作用。通过解决个人创新,预期性能,态度,享乐动机,技术焦虑,以及社会影响对性别的影响,干预措施可以有效增强对EHR系统的行为意向。至关重要的是设计针对性别的干预措施,以解决男性和女性之间的社会影响力和个人创新差异。
BACKGROUND: The implementation of Electronic Health Record (EHR) systems is a critical challenge, particularly in low-income countries, where behavioral intention plays a crucial role. To address this issue, we conducted a study to extend and apply the Unified Theory of Acceptance and Use of Technology 3 (UTAUT3) model in predicting health professionals\' behavioral intention to use EHR systems.
METHODS: A quantitative research approach was employed among 423 health professionals in Southwest Ethiopia. We assessed the validity of the proposed model through measurement and structural model statistics. Analysis was done using SPSS AMOS version 23. Hypotheses were tested using structural equation modeling (SEM) analysis, and mediation and moderation effects were evaluated. The associations between exogenous and endogenous variables were examined using standardized regression coefficients (β), 95% confidence intervals, and p-values, with a significance level of p-value < 0.05.
RESULTS: The proposed model outperformed previous UTAUT models, explaining 84.5% (squared multiple correlations (R2) = 0.845) of the variance in behavioral intention to use EHR systems. Personal innovativeness (β = 0.215, p-value < 0.018), performance expectancy (β = 0.245, p-value < 0.001), and attitude (β = 0.611, p-value < 0.001) showed significant associations to use EHR systems. Mediation analysis revealed that performance expectancy, hedonic motivation, and technology anxiety had significant indirect effects on behavioral intention. Furthermore, moderation analysis indicated that gender moderated the association between social influence, personal innovativeness, and behavioral intention.
CONCLUSIONS: The extended UTAUT3 model accurately predicts health professionals\' intention to use EHR systems and provides a valuable framework for understanding technology acceptance in healthcare. We recommend that digital health implementers and concerned bodies consider the comprehensive range of direct, indirect, and moderating effects. By addressing personal innovativeness, performance expectancy, attitude, hedonic motivation, technology anxiety, and the gender-specific impact of social influence, interventions can effectively enhance behavioral intention toward EHR systems. It is crucial to design gender-specific interventions that address the differences in social influence and personal innovativeness between males and females.